You are here: Home / Research / UCL EEE Teams are winners and finalists in NI Awards

UCL EEE Teams are winners and finalists in NI Awards

Dr Bo Tan, Visiting Researcher at UCL and Lecturer at Coventry University receives the team prize along with Dave Wilson, VP of Product Marketing at NI.

Congratulations to Emeritus Professor Karl Woodbridge (UCL EEE), Bo Tan (pictured receiving the prize), Dr Kevin Chetty (Security and Crime Science) and PhD student Qingchao Chen (EEE) who won first prize in the Biomedical Section and “Application of the Year” overall award at the prestigious National Instruments (NI) Awards in November for their WiFi activity detection. The team developed a sensing system and had previously won an award a few years ago also built on LabVIEW and USRP, which relies on sensing Doppler shift on existing Wifi signals due to movement of objects. The original system allowed them to essentially, see moving objects through walls to help police during hostage situations. However, their new award was for a different application. The team have changed their research focus from terrorists to elderly people.

In 2016, roughly 2 million over 75s lived alone in the UK and many more are in care homes. With people living longer, more people require constant monitoring for their health problems but how do we do this when there’s not enough healthcare staff? Now there are ways to monitor staff remotely, but they rely on either a wearable, wireless device which is uncomfortable, and as we just learnt, could have privacy issues if hacked, or having cameras in the room which has serious privacy issues. This is where the team’s updated system comes in. They have massively increased the accuracy, so much so that they can now measure the breathing rate of someone in the room. They have also developed a machine learning neural network that learns common actions such as sitting down, picking something up or even falling over. Once implemented in care homes, it will immediately take pressure off overworked staff. WiFi signals could immediately alert the emergency services if a loved one falls or their respiration rate dramatically changes.

Also, a team from the department’s Communication and Information Systems Group, lead by Professor Izzat Darwazeh, were finalists in the NI Awards. They have created a real-time testbed, on an industrial platform, to allow the world to investigate SEFDM. Since 2003, SEFDM has been the focus of increased interest and now as we move towards 5G, more so than ever. The group including PhD students Waseem Ozan and Hedaia Ghannam, postdoc Dr Paul Anthony Haigh and Teaching Fellow Dr Ryan Grammenos have demonstrated the world’s first real-time SEFDM system using USRP RIO and the LabVIEW Communications System Design Suite. The key innovation is in the deployment of a novel real-time channel estimation and equalisation algorithm, combined with a real-time iterative detector. Their system compresses transmitted signal bandwidths up to 60% (for BPSK) and 30% (for QPSK), offering significant bandwidth savings, thereby satisfying one of the key challenges of 5G deployment.

Event info at:

http://uk.ni.com/impactawards/2017

http://sine.ni.com/cs/app/doc/p/id/cs-17492?nisrc=RSS-labview-en